Practical Non-linear Energy Harvesting Model and Resource Allocation for SWIPT Systems (1509.02956v1)

Abstract: In this letter, we propose a practical non-linear energy harvesting model and design a resource allocation algorithm for simultaneous wireless information and power transfer (SWIPT) systems. The algorithm design is formulated as a non-convex optimization problem for the maximization of the total harvested power at energy harvesting receivers subject to minimum required signal-to-interference-plus-noise ratios (SINRs) at multiple information receivers. We transform the considered non-convex objective function from sum-of-ratios form into an equivalent objective function in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. In each iteration, a rank-constrained semidefinite program (SDP) is solved optimally by SDP relaxation. Numerical results unveil a substantial performance gain that can be achieved if the resource allocation design is based on the proposed non-linear energy harvesting model instead of the traditional linear model.

• The paper introduces a practical non-linear energy harvesting model that captures real-world circuit saturation and efficiency variations.
• It develops an iterative resource allocation algorithm using SDP relaxation to maximize harvested power while meeting SINR requirements.
• Numerical results show significant performance gains over linear models, underscoring the importance of realistic EH modeling in SWIPT systems.

Essay on "Practical Non-linear Energy Harvesting Model and Resource Allocation for SWIPT Systems"

The paper "Practical Non-linear Energy Harvesting Model and Resource Allocation for SWIPT Systems" by Elena Boshkovska, Derrick Wing Kwan Ng, Nikola Zlatanov, and Robert Schober addresses a critical aspect of simultaneous wireless information and power transfer (SWIPT) systems. The primary contribution of this work lies in the introduction of a practical non-linear energy harvesting (EH) model that more accurately reflects the behavior of real-world EH circuits, followed by the design of an optimal resource allocation algorithm.

Problem Statement

Traditional studies on SWIPT systems often assume a linear EH model where the RF-to-DC power conversion efficiency is constant, independent of the input power. This assumption does not hold in practical scenarios, where the conversion efficiency is inherently non-linear due to hardware constraints and circuit behaviors. Specifically, EH circuits exhibit varying efficiencies at different power levels, often saturating as the input power increases.

Proposed Non-linear EH Model

The authors address this gap by presenting a parametric non-linear EH model, inspired by logistic (sigmoidal) functions. This model accommodates the end-to-end non-linear characteristics of energy conversion, including saturation effects observed in practical EH circuits. The model parameters can be accurately estimated using standard curve-fitting tools based on empirical data. The novel model is defined as follows:

$$\Phi_{\mathrm{ER}_j}^{\mathrm{Practical}} = \frac{\Psi_{\mathrm{ER}_j}^{\mathrm{Practical}} - M_j\Omega_j}{1 - \Omega_j}, \quad \Omega_j=\frac{1}{1+\exp(a_jb_j)}$$

where $\Psi_{\mathrm{ER}_j}^{\mathrm{Practical}}$ represents the traditional logistic function of the received RF power $P_{\mathrm{ER}_j}$, and $a_j$, $b_j$, and $M_j$ are parameters derived from the EH circuit specifications.

Resource Allocation Algorithm

The resource allocation problem for the proposed non-linear EH model is formulated as a non-convex optimization problem aimed at maximizing the total harvested power while satisfying the minimum signal-to-interference-plus-noise ratio (SINR) requirements at multiple information receivers (IRs). The problem is inherently complex due to its non-convex and sum-of-ratios objective form.

To tackle this, the authors transform the objective function into an equivalent subtractive form, enabling the derivation of an efficient iterative resource allocation algorithm. The iterative procedure involves solving a rank-constrained semidefinite program (SDP) in each iteration, with optimal solutions derived using SDP relaxation techniques. Subsequently, a theorem is proved to ensure the equivalence of the transformed problem and the original sum-of-ratios problem, which guarantees that the derived solution is optimal.

Numerical Results and Implications

The numerical simulations demonstrate the substantial gains achieved by employing the proposed non-linear EH model over the traditional linear model. The results indicate a significant improvement in the total harvested power when resource allocation is optimized for the realistic non-linear characteristics of EH circuits. Furthermore, the SDP relaxation approach is shown to be tight, ensuring that the optimal beamforming vectors are always rank-one, thus validating the robustness of the proposed algorithm.

Practical and Theoretical Implications

Practically, this research advances the design of SWIPT systems by integrating a more realistic EH model, leading to improved power efficiency and performance. Theoretically, the paper provides a methodological framework for handling non-convex optimization problems with sum-of-ratios objectives in SWIPT systems, which can be extended to other wireless communication scenarios involving non-linearities.

Future Directions

One potential area for future research is the exploration of resource allocation algorithms that can dynamically adapt to varying channel conditions and EH model parameters. Additionally, extending the model to incorporate other non-linearities in the SWIPT system, such as impedance mismatches and environmental variations, could provide further gains in efficiency and reliability.

In conclusion, this paper makes a significant contribution to the literature on SWIPT systems by presenting a practical non-linear EH model and an optimized resource allocation algorithm, leading to enhanced system performance and efficiency. The integration of practical hardware constraints into the model showcases the importance of aligning theoretical models with real-world implementations for advancing wireless communication technologies.